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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

In [10]:
!pip install yfinance
#!pip install pandas
#!pip install requests
!pip install bs4
#!pip install plotly

Requirement already satisfied: yfinance in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (0.1.55)
Requirement already satisfied: lxml>=4.5.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (4.5.1)
Requirement already satisfied: requests>=2.20 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (2.24.0)
Requirement already satisfied: pandas>=0.24 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (1.0.5)
Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (0.0.9)
Requirement already satisfied: numpy>=1.15 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from yfinance) (1.18.5)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2020.12.5)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2.9)
Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from requests>=2.20->yfinance) (1.25.9)
Requirement already satisfied: pytz>=2017.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2020.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2.8.1)
Requirement already satisfied: six>=1.5 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)
Requirement already satisfied: bs4 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (0.0.1)
Requirement already satisfied: beautifulsoup4 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from bs4) (4.9.1)
Requirement already satisfied: soupsieve>1.2 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from beautifulsoup4->bs4) (2.0.1)
In [11]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [12]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data.Date, infer_datetime_format=True), y=stock_data.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data.Date, infer_datetime_format=True), y=revenue_data.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [13]:
tesla = yf.Ticker('TSLA')

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

In [14]:
tesla_data = tesla.history(period="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [15]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[15]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 3.800 5.000 3.508 4.778 93831500 0 0.0
1 2010-06-30 5.158 6.084 4.660 4.766 85935500 0 0.0
2 2010-07-01 5.000 5.184 4.054 4.392 41094000 0 0.0
3 2010-07-02 4.600 4.620 3.742 3.840 25699000 0 0.0
4 2010-07-06 4.000 4.000 3.166 3.222 34334500 0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue. Save the text of the response as a variable named html_data.

In [55]:
url = 'https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue'
html_data = requests.get(url).text

Parse the html data using beautiful_soup.

In [56]:
soup = BeautifulSoup(html_data,"html5lib")

Using beautiful soup extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

In [57]:
tesla_revenue = pd.DataFrame(columns=['Date', 'Revenue'])

for table in soup.find_all('table'):

    if ('Tesla Quarterly Revenue' in table.find('th').text):
        rows = table.find_all('tr')
        
        for row in rows:
            col = row.find_all('td')
            
            if col != []:
                date = col[0].text
                revenue = col[1].text.replace(',','').replace('$','')

                tesla_revenue = tesla_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)
Click here if you need help removing the dollar sign and comma ``` If you parsed the HTML table by row and column you can use the replace function on the string revenue = col[1].text.replace("$", "").replace(",", "") If you use the read_html function you can use the replace function on the string representation of the column tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace("$", "").str.replace(",", "") ```

Remove the rows in the dataframe that are empty strings or are NaN in the Revenue column. Print the entire tesla_revenue DataFrame to see if you have any.

In [58]:
tesla_revenue
Out[58]:
Date Revenue
0 2020-12-31 10744
1 2020-09-30 8771
2 2020-06-30 6036
3 2020-03-31 5985
4 2019-12-31 7384
5 2019-09-30 6303
6 2019-06-30 6350
7 2019-03-31 4541
8 2018-12-31 7226
9 2018-09-30 6824
10 2018-06-30 4002
11 2018-03-31 3409
12 2017-12-31 3288
13 2017-09-30 2985
14 2017-06-30 2790
15 2017-03-31 2696
16 2016-12-31 2285
17 2016-09-30 2298
18 2016-06-30 1270
19 2016-03-31 1147
20 2015-12-31 1214
21 2015-09-30 937
22 2015-06-30 955
23 2015-03-31 940
24 2014-12-31 957
25 2014-09-30 852
26 2014-06-30 769
27 2014-03-31 621
28 2013-12-31 615
29 2013-09-30 431
30 2013-06-30 405
31 2013-03-31 562
32 2012-12-31 306
33 2012-09-30 50
34 2012-06-30 27
35 2012-03-31 30
36 2011-12-31 39
37 2011-09-30 58
38 2011-06-30 58
39 2011-03-31 49
40 2010-12-31 36
41 2010-09-30 31
42 2010-06-30 28
43 2010-03-31 21
44 2009-12-31
45 2009-09-30 46
46 2009-06-30 27
47 2008-12-31
In [59]:
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'].astype(bool)]
Click here if you need help removing the Nan or empty strings ``` If you have NaN in the Revenue column tesla_revenue.dropna(inplace=True) If you have emtpty string in the Revenue column tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""] ```

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [60]:
tesla_revenue.tail()
Out[60]:
Date Revenue
41 2010-09-30 31
42 2010-06-30 28
43 2010-03-31 21
45 2009-09-30 46
46 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [37]:
gme = yf.Ticker('GME')

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

In [43]:
gme_data = gme.history(period='max')

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [44]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[44]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 6.480513 6.773399 6.413183 6.766666 19054000 0.0 0.0
1 2002-02-14 6.850831 6.864296 6.682506 6.733003 2755400 0.0 0.0
2 2002-02-15 6.733001 6.749833 6.632006 6.699336 2097400 0.0 0.0
3 2002-02-19 6.665671 6.665671 6.312189 6.430017 1852600 0.0 0.0
4 2002-02-20 6.463681 6.648838 6.413183 6.648838 1723200 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue. Save the text of the response as a variable named html_data.

In [45]:
url = 'https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue'
html_data = requests.get(url).text

Parse the html data using beautiful_soup.

In [46]:
soup = BeautifulSoup(html_data,"html5lib")

Using beautiful soup extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

In [49]:
gme_revenue = pd.DataFrame(columns=['Date', 'Revenue'])

for table in soup.find_all('table'):

    if ('GameStop Quarterly Revenue' in table.find('th').text):
        rows = table.find_all('tr')
        
        for row in rows:
            col = row.find_all('td')
            
            if col != []:
                date = col[0].text
                revenue = col[1].text.replace(',','').replace('$','')

                gme_revenue = gme_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [50]:
gme_revenue.tail()
Out[50]:
Date Revenue
59 2006-01-31 1667
60 2005-10-31 534
61 2005-07-31 416
62 2005-04-30 475
63 2005-01-31 709

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla')

In [61]:
make_graph(tesla_data[['Date','Close']], tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop').

In [62]:
make_graph(gme_data[['Date','Close']], gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

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